Generally described, multi-person networks, such as the Internet, facilitate the interaction of computer users and the exchange of a variety of information. More specifically, the Internet has recently seen explosive growth by virtue of its ability to link computers located throughout the world. As the Internet has grown, so has the World Wide Web (“WWW” or “Web”). The Web is a vast collection of interconnected or “hypertext” documents in HyperText Markup Language (“HTML”) that are electronically served at “Web sites” throughout the Internet.
The Web has quickly become a popular method of disseminating information due in large part to its simplicity and its ability to deliver information in a variety of formats. To make information available over the Web, a user typically composes a set of “Web pages” which are posted on a Web site by an Internet Service Provider (“ISP”). A Web site resides on a server connected to the Internet that has mass storage facilities for storing hypertext documents, a.k.a. “Web pages,” and that runs administrative software for handling requests for those stored hypertext documents. A hypertext document normally includes a number of hyperlinks, i.e., highlighted portions of text which link the document to another hypertext document possibly stored at a Web site elsewhere on the Internet. Each hyperlink is associated with a Uniform Resource Locator (“URL”) that provides the exact location of the linked document on a server connected to the Internet and describes the document. Thus, whenever a hypertext document is retrieved from any Web server, the document is considered to be retrieved from the Web.
A user is allowed to retrieve hypertext documents from the Web, i.e., a user is allowed to “surf the Web,” via a Web browser. A Web browser, such as NETSCAPE NAVIGATOR®, MICROSOFT®, Internet Explorer or phone.com's UP.link microbrowser, is a software program implemented by a Web client, e.g., the user's computer, cell phone or other client device, to provide a graphical user interface (“GUI”) to the Web. Upon request from the user via the Web browser, the Web client accesses and retrieves the desired hypertext document from the appropriate Web server using the URL for the document and a protocol known as HyperText Transfer Protocol (“HTTP”). HTTP is a higher-level protocol than TCP/IP and is designed specifically for the requirements of the Web. It is used on top of TCP/IP to transfer hypertext documents between servers and clients.
Often, a Web site provider is able to provide content, and or services, to computer users at a reduced cost, or even free, by generating advertisement revenue from one or more advertisement providers. For example, a merchant can contract with a Web site provider to pay the Web site provider to display the merchant's advertisements along with the Web site content. The contracting merchant can be generally referred to as an advertisement provider. With regard to Web sites that are accessed by a large number of users, such as a portal Web site, the Web site provider may contract with a number of advertisements providers to display an advertisement a certain number of times over a given time period, generally referred to as an advertisement campaign. Additionally, each advertisement provider may also include criteria, such as a “male, age=30 to 35,” that limits to whom the advertisement may be displayed. In such an embodiment, the Web site provider utilizes one or more criteria, such as user demographics and/or inputted keywords, obtained from the content requesting user to select an appropriate advertisement from a group of applicable advertisements. The satisfaction of advertisement provider criteria is generally referred to as a display opportunity.
In order to accommodate for large number of users requesting content and thereby requiring one or more advertisements, some Web site providers utilize an advertisement delivery system to track and deliver advertisements to the Web site provider. Often, the advertisement delivery system negotiates with various advertisement providers such that the advertisement delivery system may have to concurrently process several advertisement campaigns. Accordingly, a primary focus of the advertisement delivery system relates to the selection of an advertisement from a variety of potentially applicable advertisements so as to better comply with the contractual obligations of the current advertisement campaigns. For example, an advertisement delivery system may implement a smooth advertisement delivery system and method to better accommodate for variations in the number of display opportunities. A smooth advertisement delivery system and method may be implemented as disclosed in commonly-owned U.S. patent application Ser. No. 09/773,449, filed Jan. 31, 2001, the disclosure of which is hereby incorporated by reference.
In addition to the selection of advertisements to satisfy current advertisement campaigns, another primary focus of an advertisement delivery system relates to future display opportunity processing. In a capacity planning aspect, the advertisement delivery system utilizes an estimated number of future display opportunities to ensure that the advertisement delivery system has adequate system resources in terms of memory, processing capability, personnel to satisfy future advertisement delivery system obligations. In an available inventory aspect, the advertisement delivery system utilizes the estimated number of future display opportunities to maximize the amount of revenue that can be generated by the sale of all, or substantially all, the estimated future display opportunities.
Several advertisement delivery systems attempt to address issues relating to future display opportunities by sampling a certain percentage of current display opportunities and interpolating the sampled data to calculate future display opportunities. In accordance with this embodiment, an advertisement delivery system samples a selected percentage of the user requests for advertisements. The sampled request criteria are stored and are then statistically interpolated to predict future display opportunities. For example, a sampling of 100,000 advertisement requests at a sampling rate of 1 user request out of every 1000 user requests would generate 100 data points. If the sampled user requests produce data indicative of 10 user requests including the selection criteria “gender=male” and “age=30 to 35,” then the conventional advertisement delivery system would assume that 10% of all the user requests would include those user request criteria. Accordingly, if 1,000,000 advertisement requests were predicted for the following day, the conventional advertisement delivery system would assume that 100,000 of the requests would contain the selection criteria “gender=male” and “age=30 to 35” and would attempt to sell a sufficient number of advertisements that could be satisfied by the criteria.
Conventional sampling methods, however, can become deficient for smaller volume advertisement campaigns that have more specific user request criteria to match. For example, assume that an advertisement campaign requires that a particular set of criteria must be matched before the advertisement can be displayed and that the particular set of criteria is only appears 500 times over 350,000 user requests. Utilizing a sampling method, it would be very likely that an advertisement delivery system would detect few, if any, of the user requests satisfying the particular set of criteria. Accordingly, the conventional advertisement delivery system would incorrectly estimate the available inventory and potentially lose a portion of its revenue generating stream. Moreover, conventional sampling methods would also discourage selling smaller advertisement campaigns, as there would be little way of monitoring the performance of the advertisement delivery system.
In addition to the problems associated with smaller advertisement campaigns, a conventional sampling method may also become deficient with regard to the scalability of the advertisement delivery system. Under the conventional sampling method, the user request criteria is collected and stored for future interpolation. However, as the number of user requests increases, the amount of user request data collected and stored can impede the advertisement delivery memory and processing resources. With reference to the above example, a 1 in 1000 sampling rate yields 100 data points for 100,000 user requests. However, in larger advertisement delivery systems responding to 35,000,000 advertisement requests, the same sampling rate would yield 35,000 data points. Accordingly, the advertisement delivery system must select between reducing the data being collected and stored by increasing the sampling rate and diminishing the accuracy associated with increase sampling rate.
Conventional sampling advertisement delivery systems can also become deficient in relation to overlapping market segments. Generally described, an overlapping market segment involves the competition between two advertisement campaigns for a user request. For example, assume a user request contains the criteria “gender=male” and “age=30 to 35.” If an advertisement campaign targets “gender=male” and another, unrelated advertisement campaign targets “age=30 to 35,” an advertisement delivery could select either an advertisement from either of the two advertisement campaigns, but not both. Under a sampling method, the advertisement delivery system interpolation generally does not account for overlapping market segments, resulting in errors of predicted future display opportunity inventory.
Thus, there is a need for a system and method for more accurately and efficiently tracking and predicting advertisement display opportunities.